<![CDATA[Drug inventory planning in primary health facilities requires an accurate forecasting model because fluctuating demand can trigger stockouts or excess inventory. This study develops and evaluates a hybrid Long Short-Term Memory (LSTM) and CatBoost model for predicting the stock requirements of five essential medicines at Puskesmas Pattingalloang. The dataset consists of monthly drug dispensing records from January 2019 to December 2025. LSTM is applied as a temporal feature extractor with a three-month sliding window, while CatBoost functions as the final nonlinear regression estimator. Model performance is assessed using MAE, RMSE, MAPE, and SMAPE, with a single LSTM model used as the baseline comparison. The results show that model suitability depends on the demand pattern of each medicine. The hybrid LSTM-CatBoost model performs better on highly fluctuating medicines, particularly Paracetamol 500 mg with 24.03% SMAPE and Guaifenesin with 37.28% SMAPE. In contrast, the single LSTM model is more efficient for relatively stable demand, especially Blood Supplement Tablets with 14.09% SMAPE. Forecasting for 2026 also provides annual demand estimates that can support data-driven drug requirement planning. These findings indicate that machine learning-based forecasting is useful for pharmaceutical inventory decision support, but model selection must consider the fluctuation characteristics of each drug.]]>
Copyrights © 2026
